Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimers Disease

基于内表型网络的方法对阿尔茨海默病的计算机药物重新利用进行预测和基于群体的验证

• 批准号: 10569077
• 负责人: Feixiong Cheng
• 金额: $ 72.2万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10569077
• 关键词: AD transgenic mice; Acceleration; Address; Adherence; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease therapy; American; Amyloid; Amyloidosis; Animal Model; Bayesian Modeling; Biological Assay; Biological Availability; Blood - brain barrier anatomy; Brain; Case/Control Studies; Cause of Death; Cells; Central Nervous System; Clinical Trials; Combination Drug Therapy; Combined Modality Therapy; Complement; Complex; Computerized Medical Record; Computers; Data; Databases; Dementia; Disease; Disease Outcome; Drug Combinations; Drug Delivery Systems; Drug Targeting; Drug user; Enhancers; Evaluation; Foundations; Genes; Genome; Heritability; Hi-C; Hippocampus; Human; Human Genetics; In Situ; In Vitro; Incidence; Intelligence; Interdisciplinary Study; Investments; Knowledge; Late Onset Alzheimer Disease; Mediating; Medicine; Methodology; Microglia; Molecular; Multiomic Data; Mutate; Network-based; Neurodegenerative Disorders; Neuroglia; Pathogenesis; Patient Care; Patients; Penetration; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacologic Substance; Pharmacology; Pharmacotherapy; Physiological; Population; Positioning Attribute; Predisposition; Prevention; Preventive therapy; Proteins; Proteome; Publications; Publishing; Quality of life; Rattus; Records; Regimen; Research Personnel; Resected; Role; System; Tauopathies; Testing; Therapeutic; Transgenic Animals; United States; Validation; Visualization; Work; bioinformatics tool; brain endothelial cell; brain tissue; clinical efficacy; clinically relevant; dementia care; drug development; drug discovery; drug efficacy; drug repurposing; drug testing; effective therapy; efficacious treatment; endophenotype; functional genomics; genetic architecture; genome wide association study; genome-wide; genomic data; human genome sequencing; human interactome; improved outcome; in silico; in vivo; individual patient; informatics tool; innovation; mouse model; multiple omics; novel; novel therapeutics; patient health information; pharmacokinetic model; pharmacologic; population based; predictive tools; promoter; rational design; research and development; risk variant; side effect; single cell sequencing; single-cell RNA sequencing; symptom treatment; tau Proteins; therapeutic development; tool; transcriptome; transgenic model of alzheimer disease; trial design; web app

Although researchers have conducted more than 400 human trials for potential treatments of Alzheimer’s disease (AD) in the last two decades, the attrition rate is estimated at over 99%. Furthermore, the “one gene, one drug, one disease” reductionism-informed paradigm overlooks the inherent complexity of the disease and continues to challenge drug discovery for AD. The predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. Recent studies have suggested that AD often has common underlying mechanisms, sharing intermediate endophenotypes with many other complex diseases. These endophenotypes, such as amyloidosis and tauopathy, have essential roles in many neurodegenerative diseases. Systematic identification and characterization of novel underlying pathogenesis and disease modules, more so than mutated genes, will serve as a foundation for generating actionable targets as input for drug repurposing and rational design of combination therapy in AD. Integration of the genome, transcriptome, proteome, and the human interactome are essential for such identification. Given our preliminary results, we posit that network- based identification of novel risk genes and endophenotype modules that share degree between amyloid and tau offer unexpected opportunities for drug therapy in AD comparing to targeting amyloid and tau separately. To address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan with three specific aims. Aim 1 will explore amyloid and tau-mediated endophenotype modules for AD -- We will test the network module hypothesis for amyloid and tau using our recently developed Bayesian framework that integrates multi-omics data (i.e., genome-wide association studies [GWAS] loci, single cell sequencing, and human brain Hi-C data) and the human interactome. Aim 2 will be capable of searching existing drugs and combination therapies for AD using network proximity approaches -- We will emphasize the uses of network proximity approaches (i.e., Genome-wide Positioning Systems network [GPSnet]) to identify repurposable drugs and efficacious combination regimens. This will be accomplished by integrating AD endophenotype module findings, public drug-target databases, the human interactome, and the large-scale patient longitudinal Claims- Electronic Medical Record data (over 200 million patients from the MarketScan database). Aim 3 will evaluate brain penetration and target network engagement for repurposable drugs -- We will use the humanized in vitro blood-brain barrier, resected brain tissues (ex vivo/in situ), and transgenic AD models (i.e., TgF344-AD rats) to experimentally evaluate brain penetration and target network engagement. Evaluation will be based upon network proximity to the AD-related endophenotype modules that are relevant to maximizing efficacy and to minimizing side effects. The successful completion of this project will offer powerful network methodologies and bioinformatics tools for prediction and population-based validation of in silico drug repurposing. It will also allow for the identification of novel repurposable drugs and clinically relevant combination therapies toward AD trials.

尽管研究人员已经进行了400多次人类试验，以实现阿尔茨海默氏症的潜在治疗 在过去的二十年中，疾病（AD）的属性率估计超过99％。此外，“一个基因， 一种药物，一种疾病”简化主义的范式忽略了该疾病的继承复杂性和 继续挑战AD的药物发现。 AD的倾向涉及复杂的，多基因和 多效性遗传结构。最近的研究表明，AD通常具有共同的基础 机制，与许多其他复杂疾病共享中间的内型型。这些 内型型，例如淀粉样变性和陶氏病，在许多神经退行性疾病中具有重要作用。 新型潜在的发病机理和疾病模块的系统鉴定和表征，更多 而不是突变的基因，将作为产生可行靶标作为药物重新利用的输入的基础 和合理设计AD中的组合疗法。基因组，转录组，蛋白质组和 人互动对这种识别至关重要。鉴于我们的初步结果，我们肯定了网络 基于淀粉样蛋白和共享程度的新型风险基因和内型型模块的基于鉴定 TAU为靶向淀粉样蛋白和TAU分别提供了药物治疗的意外机会。到 解决基本假设，我们建议与 三个具体目标。 AIM 1将探索AD的淀粉样蛋白和Tau介导的内型模块 - 我们将测试 使用我们最近开发的贝叶斯框架的淀粉样蛋白和tau的网络模块假设 综合多摩学数据（即全基因组关联研究[GWAS]基因座，单细胞测序和 人脑HI-C数据）和人类互动组。 AIM 2将能够搜索现有药物，并且 使用网络接近度方法进行广告的组合疗法 - 我们将强调网络的使用 接近度方法（即全基因组定位系统网络[GPSNET]）以识别可再利用的药物 和有效的组合方案。这将通过集成AD内表型模块来实现 调查结果，公共药品目标数据库，人类互动组和大规模患者纵向主张 - 电子病历数据（来自MarketScan数据库的2亿多人）。 AIM 3将评估 大脑渗透和目标网络参与可再利用药物 - 我们将在 体外血脑屏障，切除的脑组织（离体/原位）和转基因AD模型（即TGF344-AD大鼠） 实验评估大脑渗透和目标网络参与。评估将基于 网络靠近与广告相关的内型模块，这些模块与最大化效率和与 最小化副作用。该项目的成功完成将提供强大的网络方法，并且 用于预测和基于人群的验证的生物信息学工具重新利用。它也将允许 为了鉴定新的可再利用药物和针对AD试验的临床相关组合疗法。